SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 4631–4640 of 15113 papers

TitleStatusHype
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Real World Offline Reinforcement Learning with Realistic Data Source—0
Semi-Supervised Offline Reinforcement Learning with Action-Free TrajectoriesCode1
Reinforcement Learning with Automated Auxiliary Loss Search—0
Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning—0
DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System—0
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement LearningCode1
A Unified Framework for Alternating Offline Model Training and Policy LearningCode0
Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems—0
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified